{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\Anaconda3\\lib\\requests\\__init__.py:80: RequestsDependencyWarning: urllib3 (1.25.11) or chardet (3.0.4) doesn't match a supported version!\n",
      "  RequestsDependencyWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matchzoo version 1.0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import mzcn as mz\n",
    "print('matchzoo version', mz.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`classification_task` initialized with metrics [accuracy]\n"
     ]
    }
   ],
   "source": [
    "classification_task = mz.tasks.Classification(num_classes=2)\n",
    "classification_task.metrics = ['acc']\n",
    "print(\"`classification_task` initialized with metrics\", classification_task.metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_data(tmp_data,tmp_task):\n",
    "\tdf_data = mz.pack(tmp_data,task=tmp_task)\n",
    "\treturn df_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data loading ...\n",
      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n"
     ]
    }
   ],
   "source": [
    "print('data loading ...')\n",
    "##数据集，并且进行相应的预处理\n",
    "train=pd.read_csv('./data/train_data.csv').sample(100)\n",
    "dev=pd.read_csv('./data/dev_data.csv').sample(50)\n",
    "test=pd.read_csv('./data/test_data.csv').sample(30)\n",
    "train_pack_raw = load_data(train,classification_task)\n",
    "dev_pack_raw = load_data(dev,classification_task)\n",
    "test_pack_raw=load_data(test,classification_task)\n",
    "print('data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "preprocessor = mz.models.ESIM.get_default_preprocessor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval:   0%| | 0/93 [00:00<?, ?it/s]Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 1.127 seconds.\n",
      "Prefix dict has been built successfully.\n",
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 93/93 [00:01<00:00, 56.81it/s]\n",
      "Processing text_right with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 98/98 [00:00<00:00, 191.89it/s]\n",
      "Processing text_right with append: 100%|████████████████████████████████████████████| 98/98 [00:00<00:00, 32713.23it/s]\n",
      "Building FrequencyFilter from a datapack.: 100%|████████████████████████████████████| 98/98 [00:00<00:00, 32674.23it/s]\n",
      "Processing text_right with transform: 100%|█████████████████████████████████████████| 98/98 [00:00<00:00, 32627.54it/s]\n",
      "Processing text_left with extend: 100%|█████████████████████████████████████████████| 93/93 [00:00<00:00, 93162.23it/s]\n",
      "Processing text_right with extend: 100%|████████████████████████████████████████████| 98/98 [00:00<00:00, 12276.13it/s]\n",
      "Building Vocabulary from a datapack.: 100%|██████████████████████████████████████| 860/860 [00:00<00:00, 860883.40it/s]\n",
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 93/93 [00:00<00:00, 177.92it/s]\n",
      "Processing text_right with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 98/98 [00:00<00:00, 183.97it/s]\n",
      "Processing text_right with transform: 100%|█████████████████████████████████████████| 98/98 [00:00<00:00, 49185.33it/s]\n",
      "Processing text_left with transform: 100%|██████████████████████████████████████████| 93/93 [00:00<00:00, 46497.83it/s]\n",
      "Processing text_right with transform: 100%|█████████████████████████████████████████| 98/98 [00:00<00:00, 48951.03it/s]\n",
      "Processing length_left with len: 100%|██████████████████████████████████████████████| 93/93 [00:00<00:00, 46558.88it/s]\n",
      "Processing length_right with len: 100%|█████████████████████████████████████████████| 98/98 [00:00<00:00, 32671.63it/s]\n",
      "Processing text_left with chain_transform of ChineseRemoveBlack => ChineseSimplified => ChineseEmotion => IsChinese => ChineseStopRemoval => ChineseTokenizeDemo => Tokenize => Lowercase => PuncRemoval: 100%|█| 48/48 [00:00<00:00, 116.86it/s]\n",
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      "Processing text_right with transform: 100%|█████████████████████████████████████████| 49/49 [00:00<00:00, 16330.62it/s]\n",
      "Processing text_left with transform: 100%|██████████████████████████████████████████| 48/48 [00:00<00:00, 15992.26it/s]\n",
      "Processing text_right with transform: 100%|█████████████████████████████████████████| 49/49 [00:00<00:00, 24501.78it/s]\n",
      "Processing length_left with len: 100%|███████████████████████████████████████████████| 48/48 [00:00<00:00, 6000.61it/s]\n",
      "Processing length_right with len: 100%|█████████████████████████████████████████████| 49/49 [00:00<00:00, 24510.54it/s]\n"
     ]
    }
   ],
   "source": [
    "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n",
    "dev_pack_processed = preprocessor.transform(dev_pack_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'embedding_input_dim': 339,\n",
       " 'filter_unit': <mzcn.preprocessors.units.frequency_filter.FrequencyFilter at 0x2034daa74e0>,\n",
       " 'vocab_size': 339,\n",
       " 'vocab_unit': <mzcn.preprocessors.units.vocabulary.Vocabulary at 0x2034da90d68>}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessor.context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# glove_embedding = mz.datasets.embeddings.load_glove_embedding(dimension=100)\n",
    "# term_index = preprocessor.context['vocab_unit'].state['term_index']\n",
    "# embedding_matrix = glove_embedding.build_matrix(term_index)\n",
    "# l2_norm = np.sqrt((embedding_matrix * embedding_matrix).sum(axis=1))\n",
    "# embedding_matrix = embedding_matrix / l2_norm[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trainset = mz.dataloader.Dataset(\n",
    "    data_pack=train_pack_processed,\n",
    "    mode='point'\n",
    ")\n",
    "devset = mz.dataloader.Dataset(\n",
    "    data_pack=dev_pack_processed,\n",
    "    mode='point'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "padding_callback = mz.models.ESIM.get_default_padding_callback()\n",
    "\n",
    "trainloader = mz.dataloader.DataLoader(\n",
    "    dataset=trainset,\n",
    "    stage='train',\n",
    "    callback=padding_callback\n",
    ")\n",
    "devloader = mz.dataloader.DataLoader(\n",
    "    dataset=devset,\n",
    "    stage='dev',\n",
    "    callback=padding_callback\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ESIM(\n",
      "  (embedding): Embedding(339, 100, padding_idx=0)\n",
      "  (rnn_dropout): RNNDropout(p=0.2, inplace=False)\n",
      "  (input_encoding): StackedBRNN(\n",
      "    (rnns): ModuleList(\n",
      "      (0): LSTM(100, 100, bidirectional=True)\n",
      "    )\n",
      "  )\n",
      "  (attention): BidirectionalAttention()\n",
      "  (projection): Sequential(\n",
      "    (0): Linear(in_features=800, out_features=200, bias=True)\n",
      "    (1): ReLU()\n",
      "  )\n",
      "  (composition): StackedBRNN(\n",
      "    (rnns): ModuleList(\n",
      "      (0): LSTM(200, 100, bidirectional=True)\n",
      "    )\n",
      "  )\n",
      "  (classification): Sequential(\n",
      "    (0): Dropout(p=0.2, inplace=False)\n",
      "    (1): Linear(in_features=800, out_features=200, bias=True)\n",
      "    (2): Tanh()\n",
      "    (3): Dropout(p=0.2, inplace=False)\n",
      "  )\n",
      "  (out): Linear(in_features=200, out_features=2, bias=True)\n",
      ")\n",
      "Trainable params:  757902\n"
     ]
    }
   ],
   "source": [
    "model = mz.models.ESIM()\n",
    "\n",
    "model.params['task'] = classification_task\n",
    "# model.params['embedding'] = embedding_matrix #这里是当加载glove等模型时取消该行注释\n",
    "#设置embedding系数\n",
    "model.params[\"embedding_output_dim\"]=100\n",
    "model.params[\"embedding_input_dim\"]=preprocessor.context[\"embedding_input_dim\"]\n",
    "model.params['mask_value'] = 0\n",
    "model.params['dropout'] = 0.2\n",
    "model.params['hidden_size'] = 200\n",
    "model.params['lstm_layer'] = 1\n",
    "\n",
    "model.build()\n",
    "\n",
    "print(model)\n",
    "print('Trainable params: ', sum(p.numel() for p in model.parameters() if p.requires_grad))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(),lr=1e-5)\n",
    "\n",
    "trainer = mz.trainers.Trainer(\n",
    "    model=model,\n",
    "    optimizer=optimizer,\n",
    "    trainloader=trainloader,\n",
    "    validloader=devloader,\n",
    "    validate_interval=None,\n",
    "    epochs=5\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bcab2727bf38445da03fe64d427dc760",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-3 Loss-0.706]:\n",
      "  Validation: accuracy: 0.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03d82b2f201d4679885be07f981c0a8c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-6 Loss-0.704]:\n",
      "  Validation: accuracy: 0.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "531cdadbd2c34e20b1891d663487f8bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-9 Loss-0.707]:\n",
      "  Validation: accuracy: 0.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e5ced9a4450453fb8feaccb18c9d653",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-12 Loss-0.702]:\n",
      "  Validation: accuracy: 0.4\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0faeb795afe4299a600b3eced2cc869",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=3), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Iter-15 Loss-0.702]:\n",
      "  Validation: accuracy: 0.4\n",
      "\n",
      "Cost time: 6.73413872718811s\n"
     ]
    }
   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2986"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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